In this study, the efficacy of the automated deep convolutional neural network (DCNN) was evaluated for the classification of dental implant systems (DISs) and the accuracy of the performance was compared against that of dental professionals using dental radiographic images collected from three dental hospitals. A total of 11,980 panoramic and periapical radiographic images with six different types of DISs were divided into training (n = 9584) and testing (n = 2396) datasets. To compare the accuracy of the trained automated DCNN with dental professionals (including six board-certified periodontists, eight periodontology residents, and 11 residents not specialized in periodontology), 180 images were randomly selected from the test dataset. The accuracy of the automated DCNN based on the AUC, Youden index, sensitivity, and specificity, were 0.954, 0.808, 0.955, and 0.853, respectively. The automated DCNN outperformed most of the participating dental professionals, including board-certified periodontists, periodontal residents, and residents not specialized in periodontology. The automated DCNN was highly effective in classifying similar shapes of different types of DISs based on dental radiographic images. Further studies are necessary to determine the efficacy and feasibility of applying an automated DCNN in clinical practice.Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.Malnutrition is caused either by cancer itself or by its treatment, and affects the clinical outcome, the quality of life (QOL), and the overall survival (OS) of the patient. However, malnutrition in children with cancer should not be accepted or tolerated as an inevitable procedure at any stage of the disease. A review of the international literature from 2014 to 2019 was performed. Despite the difficulty of accurately assessing the prevalence of malnutrition, poor nutritional status has adverse effects from diagnosis to subsequent survival. Nutritional status (NS) at diagnosis relates to undernutrition, while correlations with clinical outcome are still unclear. Malnutrition adversely affects health-related quality of life (HRQOL) in children with cancer and collective evidence constantly shows poor nutritional quality in childhood cancer survivors (CCSs). https://www.selleckchem.com/CDK.html Nutritional assessment and early intervention in pediatric cancer patients could minimize the side effects of treatment, improve their survival, and reduce the risk of nutritional morbidity with a positive impact on QOL, in view of the potentially manageable nature of this risk factor.Chronic pain disorders have been associated separately with neuropsychiatric conditions such as depression and alcohol abuse. However, in individuals who suffer from non-cancer chronic pain disorders, it is not clear if the burden of depressive disorders is similar for those with and without a history of alcohol abuse. Using data from the Collaborative Psychiatric Epidemiology Surveys (CPES), we found depressive disorders to have a high burden in men and women with a history of alcohol abuse, independently of the presence or absence of chronic pain. We also found that, although the incidence of persistent depressive disorder was comparable in men and women with a history of alcohol abuse, and significantly higher than in control men and women, the incidence of a major depressive episode was higher in women with a history of alcohol abuse independently of the presence or absence of chronic pain. The age of onset of depressive disorders, independently of pain status, was younger for individuals with a history of alcohol abuse. The findings of this study have important implications for the clinical management of individuals who suffer from chronic pain comorbidly with depression and/or alcohol abuse.As a multifactorial cause, gastric ulceration-mediated diarrhea is widely prevalent in the weaned piglets, impairing pig health and economic benefits. With full implementation of antibiotic stewardship programs in China, Bacillus cereus (B. cereus) and Aspergillus fumigatus (A. fumigatus) were identified frequently in porcine feedstuffs and feeds of the animal industry. Association between feed-borne B. cereus and frequent diarrhea remains unclear. In the present study, we conducted a survey of B. cereus and A. fumigatus from feeds and feedstuffs in pig farms during hot season. Interestingly, B. cereus, B. subtilis, B. licheniformis and B. thuringinesis were isolated and identified from piglets' starter meals to sow feeds, accounting for 56.1%, 23.7%, 13.7% and 6.5%, respectively. Obviously, both B. cereus and B. subtili were dominant contaminants in the survey. In an in vitro study, Deoxynivalenol (DON) contents were determined in a dose-dependent manner post fermentation with B. cereus (405 and DawuC). Subsequently, 36 weaned piglets were randomly assigned to four groups and the piglets simultaneously received the combination of virulent B.